Source:http://linkedlifedata.com/resource/pubmed/id/16608061
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
4
|
| pubmed:dateCreated |
2006-4-12
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| pubmed:abstractText |
Our goal is to enhance the ability to differentiate normal lung from subtle pathologies via multidetector row CT (MDCT) by extending a two-dimensional (2-D) texturebased tissue classification [adaptive multiple feature method (AMFM)] to use three-dimensional (3-D) texture features. We performed MDCT on 34 humans and classified volumes of interest (VOIs) in the MDCT images into five categories: EC, emphysema in severe chronic obstructive pulmonary disease (COPD); MC, mild emphysema in mild COPD; NC, normal appearing lung in mild COPD; NN, normal appearing lung in normal nonsmokers; and NS, normal appearing lung in normal smokers. COPD severity was based upon pulmonary function tests (PFTs). Airways and vessels were excluded from VOIs; 24 3-D texture features were calculated; and a Bayesian classifier was used for discrimination. A leave-one-out method was employed for validation. Sensitivity of the four-class classification in the form of 3-D/2-D was: EC: 85%/71%, MC: 90%/82%; NC: 88%/50%; NN: 100%/60%. Sensitivity and specificity for NN using a two-class classification of NN and NS in the form of 3-D/2-D were: 99%/72% and 100%/75%, respectively. We conclude that 3-D AMFM analysis of lung parenchyma improves discrimination compared to 2-D AMFM of the same VOIs. Furthermore, our results suggest that the 3-D AMFM may provide a means of discriminating subtle differences between smokers and nonsmokers both with normal PFTs.
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| pubmed:grant | |
| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:status |
MEDLINE
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| pubmed:month |
Apr
|
| pubmed:issn |
0278-0062
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| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:volume |
25
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
464-75
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| pubmed:dateRevised |
2007-11-14
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| pubmed:meshHeading |
pubmed-meshheading:16608061-Algorithms,
pubmed-meshheading:16608061-Artifacts,
pubmed-meshheading:16608061-Artificial Intelligence,
pubmed-meshheading:16608061-Female,
pubmed-meshheading:16608061-Humans,
pubmed-meshheading:16608061-Imaging, Three-Dimensional,
pubmed-meshheading:16608061-Information Storage and Retrieval,
pubmed-meshheading:16608061-Male,
pubmed-meshheading:16608061-Middle Aged,
pubmed-meshheading:16608061-Pattern Recognition, Automated,
pubmed-meshheading:16608061-Pulmonary Emphysema,
pubmed-meshheading:16608061-Radiation Dosage,
pubmed-meshheading:16608061-Radiographic Image Enhancement,
pubmed-meshheading:16608061-Radiographic Image Interpretation, Computer-Assisted,
pubmed-meshheading:16608061-Reproducibility of Results,
pubmed-meshheading:16608061-Sensitivity and Specificity,
pubmed-meshheading:16608061-Severity of Illness Index,
pubmed-meshheading:16608061-Smoking,
pubmed-meshheading:16608061-Stochastic Processes,
pubmed-meshheading:16608061-Tomography, X-Ray Computed,
pubmed-meshheading:16608061-Transducers
|
| pubmed:year |
2006
|
| pubmed:articleTitle |
MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies.
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| pubmed:affiliation |
Iowa Comprehension Lung Imaging Center, University of Iowa, Iowa City, IA 52240, USA.
|
| pubmed:publicationType |
Journal Article,
Controlled Clinical Trial,
Research Support, Non-U.S. Gov't,
Research Support, N.I.H., Extramural
|